Reaching the Peta-Computing: 163.8 TOPS Through Multidimensional AWGR-Based Accelerators

Christos Pappas, Theodoros Moschos, Antonios Prapas, Manos Kirtas, Miltiadis Moralis-Pegios, Apostolos Tsakyridis, Odysseas Asimopoulos, Nikolaos Passalis, Anastasios Tefas, Nikos Pleros

Abstract: The prowess of Artificial Intelligence (AI) and Large Language Models (LLMs) to compute the exponentially growing data, coupled with the digital electronic AI computing systems reaching their physical plateaus, have stimulated extensive research into next-gen AI accelerators. Optical Neural Networks (ONNs) have been among the most favored candidates, since the unparalleled speed of light can provide massive compute parallelism by the physical dimensions of time, wavelength, and space, offering higher computational speeds and lower power consumption. In this paper, we experimentally demonstrate novel time-space-wavelength multiplexed arrayed waveguided gratings router (AWGR)-based architectures exploited as photonic matrix- and tensor-multiplier engines achieving a total computational power of up to 163.8 TOPS. This marks a substantial ∼15x improvement over state-of-the-art waveguide-based optical accelerators. The two demonstrators were tested for three different NN applications, under two experimental testbeds. The matrix-multiplier was evaluated as a fully connected NN (FCNN), classifying images from the FMNIST dataset. An 8 × 8 AWGR was employed for providing the passive routing, with modulators driven at 10Gbaud, exhibiting a hardware-based inference accuracy of 87.1% with respect to the software. The tensor-multiplier, using a 16 × 16 AWGR, was evaluated as a FCNN and a convolutional NN (CNN), detecting DDoS attacks and classifying handwritten digits at 20 Gbaud. The experimental results exhibited a Cohen's kappa score of 0.799 for the DDoS detection and a classification accuracy of 93.35%, respectively.

External IDs:doi:10.1109/jlt.2025.3532315